"""
The streams module defines the streams API that allows visualizations to
generate and respond to events, originating either in Python on the
server-side or in Javascript in the Jupyter notebook (client-side).
"""
import weakref
from numbers import Number
from collections import defaultdict
from contextlib import contextmanager
from functools import partial
from itertools import groupby
from types import FunctionType
import param
import numpy as np
from .core import util
from .core.ndmapping import UniformNdMapping
# Types supported by Pointer derived streams
pointer_types = (Number, util.basestring, tuple)+util.datetime_types
[docs]@contextmanager
def triggering_streams(streams):
"""
Temporarily declares the streams as being in a triggered state.
Needed by DynamicMap to determine whether to memoize on a Callable,
i.e. if a stream has memoization disabled and is in triggered state
Callable should disable lookup in the memoization cache. This is
done by the dynamicmap_memoization context manager.
"""
for stream in streams:
stream._triggering = True
try:
yield
except:
raise
finally:
for stream in streams:
stream._triggering = False
[docs]class Stream(param.Parameterized):
"""
A Stream is simply a parameterized object with parameters that
change over time in response to update events and may trigger
downstream events on its subscribers. The Stream parameters can be
updated using the update method, which will optionally trigger the
stream. This will notify the subscribers which may be supplied as
a list of callables or added later using the add_subscriber
method. The subscribers will be passed a dictionary mapping of the
parameters of the stream, which are available on the instance as
the ``contents``.
Depending on the plotting backend certain streams may
interactively subscribe to events and changes by the plotting
backend. For this purpose use the LinkedStream baseclass, which
enables the linked option by default. A source for the linking may
be supplied to the constructor in the form of another viewable
object specifying which part of a plot the data should come from.
The transient option allows treating stream events as discrete
updates, resetting the parameters to their default after the
stream has been triggered. A downstream callback can therefore
determine whether a stream is active by checking whether the
stream values match the default (usually None).
The Stream class is meant for subclassing and subclasses should
generally add one or more parameters but may also override the
transform and reset method to preprocess parameters before they
are passed to subscribers and reset them using custom logic
respectively.
"""
# Mapping from a source to a list of streams
# WeakKeyDictionary to allow garbage collection
# of unreferenced sources
registry = weakref.WeakKeyDictionary()
# Mapping to define callbacks by backend and Stream type.
# e.g. Stream._callbacks['bokeh'][Stream] = Callback
_callbacks = defaultdict(dict)
[docs] @classmethod
def define(cls, name, **kwargs):
"""
Utility to quickly and easily declare Stream classes. Designed
for interactive use such as notebooks and shouldn't replace
parameterized class definitions in source code that is imported.
Takes a stream class name and a set of keywords where each
keyword becomes a parameter. If the value is already a
parameter, it is simply used otherwise the appropriate parameter
type is inferred and declared, using the value as the default.
Supported types: bool, int, float, str, dict, tuple and list
"""
params = {'name': param.String(default=name)}
for k, v in kwargs.items():
kws = dict(default=v, constant=True)
if isinstance(v, param.Parameter):
params[k] = v
elif isinstance(v, bool):
params[k] = param.Boolean(**kws)
elif isinstance(v, int):
params[k] = param.Integer(**kws)
elif isinstance(v, float):
params[k] = param.Number(**kws)
elif isinstance(v, str):
params[k] = param.String(**kws)
elif isinstance(v, dict):
params[k] = param.Dict(**kws)
elif isinstance(v, tuple):
params[k] = param.Tuple(**kws)
elif isinstance(v, list):
params[k] = param.List(**kws)
elif isinstance(v, np.ndarray):
params[k] = param.Array(**kws)
else:
params[k] = param.Parameter(**kws)
# Dynamic class creation using type
return type(name, (Stream,), params)
[docs] @classmethod
def trigger(cls, streams):
"""
Given a list of streams, collect all the stream parameters into
a dictionary and pass it to the union set of subscribers.
Passing multiple streams at once to trigger can be useful when a
subscriber may be set multiple times across streams but only
needs to be called once.
"""
# Union of stream contents
items = [stream.contents.items() for stream in set(streams)]
union = [kv for kvs in items for kv in kvs]
klist = [k for k, _ in union]
key_clashes = []
for k, v in union:
key_count = klist.count(k)
try:
value_count = union.count((k, v))
except Exception:
# If we can't compare values we assume they are not equal
value_count = 1
if key_count > 1 and key_count > value_count and k not in key_clashes:
key_clashes.append(k)
if key_clashes:
print('Parameter name clashes for keys %r' % key_clashes)
# Group subscribers by precedence while keeping the ordering
# within each group
subscriber_precedence = defaultdict(list)
for stream in streams:
stream._on_trigger()
for precedence, subscriber in stream._subscribers:
subscriber_precedence[precedence].append(subscriber)
sorted_subscribers = sorted(subscriber_precedence.items(), key=lambda x: x[0])
subscribers = util.unique_iterator([s for _, subscribers in sorted_subscribers
for s in subscribers])
with triggering_streams(streams):
for subscriber in subscribers:
subscriber(**dict(union))
for stream in streams:
with util.disable_constant(stream):
if stream.transient:
stream.reset()
def _on_trigger(self):
"""Called when a stream has been triggered"""
@classmethod
def _process_streams(cls, streams):
"""
Processes a list of streams promoting Parameterized objects and
methods to Param based streams.
"""
parameterizeds = defaultdict(set)
valid, invalid = [], []
for s in streams:
if isinstance(s, partial):
s = s.func
if isinstance(s, Stream):
pass
elif isinstance(s, param.Parameter):
s = Params(s.owner, [s.name])
elif isinstance(s, param.Parameterized):
s = Params(s)
elif util.is_param_method(s):
if not hasattr(s, "_dinfo"):
continue
s = ParamMethod(s)
elif isinstance(s, FunctionType) and hasattr(s, "_dinfo"):
deps = s._dinfo
dep_params = list(deps['dependencies']) + list(deps.get('kw', {}).values())
rename = {(p.owner, p.name): k for k, p in deps.get('kw', {}).items()}
s = Params(parameters=dep_params, rename=rename)
else:
invalid.append(s)
continue
if isinstance(s, Params):
pid = id(s.parameterized)
overlap = (set(s.parameters) & parameterizeds[pid])
if overlap:
pname = type(s.parameterized).__name__
param.main.param.warning(
'The %s parameter(s) on the %s object have '
'already been supplied in another stream. '
'Ensure that the supplied streams only specify '
'each parameter once, otherwise multiple '
'events will be triggered when the parameter '
'changes.' % (sorted([p.name for p in overlap]), pname))
parameterizeds[pid] |= set(s.parameters)
valid.append(s)
return valid, invalid
def __init__(self, rename={}, source=None, subscribers=[], linked=False,
transient=False, **params):
"""
The rename argument allows multiple streams with similar event
state to be used by remapping parameter names.
Source is an optional argument specifying the HoloViews
datastructure that the stream receives events from, as supported
by the plotting backend.
Some streams are configured to automatically link to the source
plot, to disable this set linked=False
"""
# Source is stored as a weakref to allow it to be garbage collected
self._source = None if source is None else weakref.ref(source)
self._subscribers = []
for subscriber in subscribers:
self.add_subscriber(subscriber)
self.linked = linked
self.transient = transient
# Whether this stream is currently triggering its subscribers
self._triggering = False
# The metadata may provide information about the currently
# active event, i.e. the source of the stream values may
# indicate where the event originated from
self._metadata = {}
super(Stream, self).__init__(**params)
self._rename = self._validate_rename(rename)
if source is not None:
if source in self.registry:
self.registry[source].append(self)
else:
self.registry[source] = [self]
[docs] def clone(self):
"""Return new stream with identical properties and no subscribers"""
return type(self)(**self.contents)
@property
def subscribers(self):
"""Property returning the subscriber list"""
return [s for p, s in sorted(self._subscribers, key=lambda x: x[0])]
[docs] def clear(self, policy='all'):
"""
Clear all subscribers registered to this stream.
The default policy of 'all' clears all subscribers. If policy is
set to 'user', only subscribers defined by the user are cleared
(precedence between zero and one). A policy of 'internal' clears
subscribers with precedence greater than unity used internally
by HoloViews.
"""
policies = ['all', 'user', 'internal']
if policy not in policies:
raise ValueError('Policy for clearing subscribers must be one of %s' % policies)
if policy == 'all':
remaining = []
elif policy == 'user':
remaining = [(p, s) for (p, s) in self._subscribers if p > 1]
else:
remaining = [(p, s) for (p, s) in self._subscribers if p <= 1]
self._subscribers = remaining
[docs] def reset(self):
"""
Resets stream parameters to their defaults.
"""
with util.disable_constant(self):
for k, p in self.param.objects('existing').items():
if k != 'name':
setattr(self, k, p.default)
[docs] def add_subscriber(self, subscriber, precedence=0):
"""
Register a callable subscriber to this stream which will be
invoked either when event is called or when this stream is
passed to the trigger classmethod.
Precedence allows the subscriber ordering to be
controlled. Users should only add subscribers with precedence
between zero and one while HoloViews itself reserves the use of
higher precedence values. Subscribers with high precedence are
invoked later than ones with low precedence.
"""
if not callable(subscriber):
raise TypeError('Subscriber must be a callable.')
self._subscribers.append((precedence, subscriber))
def _validate_rename(self, mapping):
param_names = [k for k in self.param if k != 'name']
for k, v in mapping.items():
if k not in param_names:
raise KeyError('Cannot rename %r as it is not a stream parameter' % k)
if k != v and v in param_names:
raise KeyError('Cannot rename to %r as it clashes with a '
'stream parameter of the same name' % v)
return mapping
[docs] def rename(self, **mapping):
"""
The rename method allows stream parameters to be allocated to
new names to avoid clashes with other stream parameters of the
same name. Returns a new clone of the stream instance with the
specified name mapping.
"""
params = {k: v for k, v in self.param.get_param_values() if k != 'name'}
return self.__class__(rename=mapping,
source=(self._source() if self._source else None),
linked=self.linked, **params)
@property
def source(self):
return self._source() if self._source else None
@source.setter
def source(self, source):
if self.source is not None:
source_list = self.registry[self.source]
if self in source_list:
source_list.remove(self)
if not source_list:
self.registry.pop(self.source)
if source is None:
self._source = None
return
self._source = weakref.ref(source)
if source in self.registry:
self.registry[source].append(self)
else:
self.registry[source] = [self]
@property
def contents(self):
filtered = {k: v for k, v in self.param.get_param_values() if k != 'name'}
return {self._rename.get(k, k): v for (k, v) in filtered.items()
if self._rename.get(k, True) is not None}
@property
def hashkey(self):
"""
The object the memoization hash is computed from. By default
returns the stream contents but can be overridden to provide
a custom hash key.
"""
return self.contents
def _set_stream_parameters(self, **kwargs):
"""
Sets the stream parameters which are expected to be declared
constant.
"""
with util.disable_constant(self):
self.param.set_param(**kwargs)
[docs] def event(self, **kwargs):
"""
Update the stream parameters and trigger an event.
"""
self.update(**kwargs)
self.trigger([self])
[docs] def update(self, **kwargs):
"""
The update method updates the stream parameters (without any
renaming applied) in response to some event. If the stream has a
custom transform method, this is applied to transform the
parameter values accordingly.
To update and trigger, use the event method.
"""
self._set_stream_parameters(**kwargs)
transformed = self.transform()
if transformed:
self._set_stream_parameters(**transformed)
def __repr__(self):
cls_name = self.__class__.__name__
kwargs = ','.join('%s=%r' % (k, v)
for (k, v) in self.param.get_param_values() if k != 'name')
if not self._rename:
return '%s(%s)' % (cls_name, kwargs)
else:
return '%s(%r, %s)' % (cls_name, self._rename, kwargs)
def __str__(self):
return repr(self)
[docs]class Counter(Stream):
"""
Simple stream that automatically increments an integer counter
parameter every time it is updated.
"""
counter = param.Integer(default=0, constant=True, bounds=(0, None))
[docs]class Pipe(Stream):
"""
A Stream used to pipe arbitrary data to a callback.
Unlike other streams memoization can be disabled for a
Pipe stream (and is disabled by default).
"""
data = param.Parameter(default=None, constant=True, doc="""
Arbitrary data being streamed to a DynamicMap callback.""")
def __init__(self, data=None, memoize=False, **params):
super(Pipe, self).__init__(data=data, **params)
self._memoize_counter = 0
[docs] def send(self, data):
"""
A convenience method to send an event with data without
supplying a keyword.
"""
self.event(data=data)
def _on_trigger(self):
self._memoize_counter += 1
@property
def hashkey(self):
return {'_memoize_key': self._memoize_counter}
[docs]class Buffer(Pipe):
"""
Buffer allows streaming and accumulating incoming chunks of rows
from tabular datasets. The data may be in the form of a pandas
DataFrame, 2D arrays of rows and columns or dictionaries of column
arrays. Buffer will accumulate the last N rows, where N is defined
by the specified ``length``. The accumulated data is then made
available via the ``data`` parameter.
A Buffer may also be instantiated with a streamz.StreamingDataFrame
or a streamz.StreamingSeries, it will automatically subscribe to
events emitted by a streamz object.
When streaming a DataFrame will reset the DataFrame index by
default making it available to HoloViews elements as dimensions,
this may be disabled by setting index=False.
The ``following`` argument determines whether any plot which is
subscribed to this stream will update the axis ranges when an
update is pushed. This makes it possible to control whether zooming
is allowed while streaming.
"""
def __init__(self, data, length=1000, index=True, following=True, **params):
if (util.pd and isinstance(data, util.pd.DataFrame)):
example = data
elif isinstance(data, np.ndarray):
if data.ndim != 2:
raise ValueError("Only 2D array data may be streamed by Buffer.")
example = data
elif isinstance(data, dict):
if not all(isinstance(v, np.ndarray) for v in data.values()):
raise ValueError("Data in dictionary must be of array types.")
elif len(set(len(v) for v in data.values())) > 1:
raise ValueError("Columns in dictionary must all be the same length.")
example = data
else:
try:
from streamz.dataframe import StreamingDataFrame, StreamingSeries
loaded = True
except ImportError:
try:
from streamz.dataframe import DataFrame as StreamingDataFrame, Series as StreamingSeries
loaded = True
except ImportError:
loaded = False
if not loaded or not isinstance(data, (StreamingDataFrame, StreamingSeries)):
raise ValueError("Buffer must be initialized with pandas DataFrame, "
"streamz.StreamingDataFrame or streamz.StreamingSeries.")
elif isinstance(data, StreamingSeries):
data = data.to_frame()
example = data.example
data.stream.sink(self.send)
self.sdf = data
if index and (util.pd and isinstance(example, util.pd.DataFrame)):
example = example.reset_index()
params['data'] = example
super(Buffer, self).__init__(**params)
self.length = length
self.following = following
self._chunk_length = 0
self._count = 0
self._index = index
[docs] def verify(self, x):
""" Verify consistency of dataframes that pass through this stream """
if type(x) != type(self.data):
raise TypeError("Input expected to be of type %s, got %s." %
(type(self.data).__name__, type(x).__name__))
elif isinstance(x, np.ndarray):
if x.ndim != 2:
raise ValueError('Streamed array data must be two-dimensional')
elif x.shape[1] != self.data.shape[1]:
raise ValueError("Streamed array data expeced to have %d columns, "
"got %d." % (self.data.shape[1], x.shape[1]))
elif util.pd and isinstance(x, util.pd.DataFrame) and list(x.columns) != list(self.data.columns):
raise IndexError("Input expected to have columns %s, got %s" %
(list(self.data.columns), list(x.columns)))
elif isinstance(x, dict):
if any(c not in x for c in self.data):
raise IndexError("Input expected to have columns %s, got %s" %
(sorted(self.data.keys()), sorted(x.keys())))
elif len(set(len(v) for v in x.values())) > 1:
raise ValueError("Input columns expected to have the "
"same number of rows.")
[docs] def clear(self):
"Clears the data in the stream"
if isinstance(self.data, np.ndarray):
data = self.data[:, :0]
elif util.pd and isinstance(self.data, util.pd.DataFrame):
data = self.data.iloc[:0]
elif isinstance(self.data, dict):
data = {k: v[:0] for k, v in self.data.items()}
with util.disable_constant(self):
self.data = data
self.send(data)
def _concat(self, data):
"""
Concatenate and slice the accepted data types to the defined
length.
"""
if isinstance(data, np.ndarray):
data_length = len(data)
if data_length < self.length:
prev_chunk = self.data[-(self.length-data_length):]
data = np.concatenate([prev_chunk, data])
elif data_length > self.length:
data = data[-self.length:]
elif util.pd and isinstance(data, util.pd.DataFrame):
data_length = len(data)
if data_length < self.length:
prev_chunk = self.data.iloc[-(self.length-data_length):]
data = util.pd.concat([prev_chunk, data])
elif data_length > self.length:
data = data.iloc[-self.length:]
elif isinstance(data, dict) and data:
data_length = len(list(data.values())[0])
new_data = {}
for k, v in data.items():
if data_length < self.length:
prev_chunk = self.data[k][-(self.length-data_length):]
new_data[k] = np.concatenate([prev_chunk, v])
elif data_length > self.length:
new_data[k] = v[-self.length:]
else:
new_data[k] = v
data = new_data
self._chunk_length = data_length
return data
[docs] def update(self, **kwargs):
"""
Overrides update to concatenate streamed data up to defined length.
"""
data = kwargs.get('data')
if data is not None:
if (util.pd and isinstance(data, util.pd.DataFrame) and
list(data.columns) != list(self.data.columns) and self._index):
data = data.reset_index()
self.verify(data)
kwargs['data'] = self._concat(data)
self._count += 1
super(Buffer, self).update(**kwargs)
@property
def hashkey(self):
return {'hash': self._count}
[docs]class Params(Stream):
"""
A Stream that watches the changes in the parameters of the supplied
Parameterized objects and triggers when they change.
"""
parameterized = param.ClassSelector(class_=(param.Parameterized,
param.parameterized.ParameterizedMetaclass),
constant=True, allow_None=True, doc="""
Parameterized instance to watch for parameter changes.""")
parameters = param.List([], constant=True, doc="""
Parameters on the parameterized to watch.""")
def __init__(self, parameterized=None, parameters=None, watch=True, watch_only=False, **params):
if util.param_version < '1.8.0' and watch:
raise RuntimeError('Params stream requires param version >= 1.8.0, '
'to support watching parameters.')
if parameters is None:
parameters = [parameterized.param[p] for p in parameterized.param if p != 'name']
else:
parameters = [p if isinstance(p, param.Parameter) else parameterized.param[p]
for p in parameters]
if 'rename' in params:
rename = {}
owners = [p.owner for p in parameters]
for k, v in params['rename'].items():
if isinstance(k, tuple):
rename[k] = v
else:
rename.update({(o, k): v for o in owners})
params['rename'] = rename
self._watch_only = watch_only
super(Params, self).__init__(parameterized=parameterized, parameters=parameters, **params)
self._memoize_counter = 0
self._events = []
self._watchers = []
if watch:
# Subscribe to parameters
keyfn = lambda x: id(x.owner)
for _, group in groupby(sorted(parameters, key=keyfn)):
group = list(group)
watcher = group[0].owner.param.watch(self._watcher, [p.name for p in group])
self._watchers.append(watcher)
[docs] def unwatch(self):
"""Stop watching parameters."""
for watcher in self._watchers:
watcher.inst.param.unwatch(watcher)
self._watchers.clear()
[docs] @classmethod
def from_params(cls, params, **kwargs):
"""Returns Params streams given a dictionary of parameters
Args:
params (dict): Dictionary of parameters
Returns:
List of Params streams
"""
key_fn = lambda x: id(x[1].owner)
streams = []
for _, group in groupby(sorted(params.items(), key=key_fn), key_fn):
group = list(group)
inst = [p.owner for _, p in group][0]
if not isinstance(inst, param.Parameterized):
continue
names = [p.name for _, p in group]
rename = {p.name: n for n, p in group}
streams.append(cls(inst, names, rename=rename, **kwargs))
return streams
def _validate_rename(self, mapping):
pnames = [p.name for p in self.parameters]
for k, v in mapping.items():
n = k[1] if isinstance(k, tuple) else k
if n not in pnames:
raise KeyError('Cannot rename %r as it is not a stream parameter' % n)
if n != v and v in pnames:
raise KeyError('Cannot rename to %r as it clashes with a '
'stream parameter of the same name' % v)
return mapping
def _watcher(self, *events):
try:
self._events = list(events)
self.trigger([self])
except:
raise
finally:
self._events = []
def _on_trigger(self):
if any(e.type == 'triggered' for e in self._events):
self._memoize_counter += 1
@property
def hashkey(self):
hashkey = {(p.owner, p.name): getattr(p.owner, p.name) for p in self.parameters}
hashkey = {' '.join([o.name, self._rename.get((o, n), n)]): v for (o, n), v in hashkey.items()
if self._rename.get((o, n), True) is not None}
hashkey['_memoize_key'] = self._memoize_counter
return hashkey
[docs] def update(self, **kwargs):
for k, v in kwargs.items():
setattr(self.parameterized, k, v)
@property
def contents(self):
if self._watch_only:
return {}
filtered = {(p.owner, p.name): getattr(p.owner, p.name) for p in self.parameters}
return {self._rename.get((o, n), n): v for (o, n), v in filtered.items()
if self._rename.get((o, n), True) is not None}
[docs]class ParamMethod(Params):
"""
A Stream that watches the parameter dependencies on a method of
a parameterized class and triggers when one of the parameters
change.
"""
def __init__(self, parameterized, parameters=None, watch=True, **params):
if not util.is_param_method(parameterized):
raise ValueError('ParamMethod stream expects a method on a '
'parameterized class, found %s.'
% type(parameterized).__name__)
method = parameterized
parameterized = util.get_method_owner(parameterized)
if not parameters:
parameters = [p.pobj for p in parameterized.param.params_depended_on(method.__name__)]
params['watch_only'] = True
super(ParamMethod, self).__init__(parameterized, parameters, watch, **params)
[docs]class Derived(Stream):
"""
A Stream that watches the parameters of one or more input streams and produces
a result that is a pure function of the input stream values.
If exclusive=True, then all streams except the most recently updated are cleared.
"""
def __init__(self, input_streams, exclusive=False, **params):
super(Derived, self).__init__(**params)
self.input_streams = []
self._updating = set()
self._register_streams(input_streams)
self.exclusive = exclusive
self.update()
def _register_streams(self, streams):
"""
Register callbacks to watch for changes to input streams
"""
for stream in streams:
self._register_stream(stream)
def _register_stream(self, stream):
i = len(self.input_streams)
def perform_update(stream_index=i, **kwargs):
if stream_index in self._updating:
return
# If exclusive, reset other stream values before triggering event
if self.exclusive:
for j, input_stream in enumerate(self.input_streams):
if stream_index != j:
input_stream.reset()
self._updating.add(j)
try:
input_stream.event()
finally:
self._updating.remove(j)
self.event()
stream.add_subscriber(perform_update)
self.input_streams.append(stream)
def _unregister_input_streams(self):
"""
Unregister callbacks on input streams and clear input streams list
"""
for stream in self.input_streams:
stream.source = None
stream.clear()
self.input_streams.clear()
@property
def constants(self):
"""
Dict of constants for this instance that should be passed to transform_function
Constant values must not change in response to changes in the values of the
input streams. They may, however, change in response to other stream property
updates. For example, these values may change if the Stream's source element
changes
"""
return {}
def __del__(self):
self._unregister_input_streams()
[docs]class History(Stream):
"""
A Stream that maintains a history of the values of a single input stream
"""
values = param.List(constant=True, doc="""
List containing the historical values of the input stream""")
def __init__(self, input_stream, **params):
super(History, self).__init__(**params)
self.input_stream = input_stream
self._register_input_stream()
# Trigger event on input stream after registering so that current value is
# added to our values list
self.input_stream.event()
[docs] def clone(self):
return type(self)(self.input_stream.clone(), **self.contents)
def clear_history(self):
del self.values[:]
def _register_input_stream(self):
"""
Register callback on input_stream to watch for changes
"""
def perform_update(**kwargs):
self.values.append(kwargs)
self.event()
self.input_stream.add_subscriber(perform_update)
def __del__(self):
self.input_stream.source = None
self.input_stream.clear()
del self.values[:]
[docs]class SelectionExpr(Derived):
selection_expr = param.Parameter(default=None, constant=True)
bbox = param.Dict(default=None, constant=True)
region_element = param.Parameter(default=None, constant=True)
def __init__(self, source, include_region=True, **params):
from .element import Element
from .core.spaces import DynamicMap
from .plotting.util import initialize_dynamic
self._index_cols = params.pop('index_cols', None)
self.include_region = include_region
if isinstance(source, DynamicMap):
initialize_dynamic(source)
if not ((isinstance(source, DynamicMap) and issubclass(source.type, Element))
or isinstance(source, Element)):
raise ValueError(
"The source of SelectionExpr must be an instance of an "
"Element subclass or a DynamicMap that returns such an "
"instance. Received value of type {typ}: {val}".format(
typ=type(source), val=source)
)
input_streams = self._build_selection_streams(source)
super(SelectionExpr, self).__init__(
source=source, input_streams=input_streams, exclusive=True, **params
)
[docs] def clone(self):
return type(self)(self.source, **self.contents)
def _build_selection_streams(self, source):
from holoviews.core.spaces import DynamicMap
if isinstance(source, DynamicMap):
element_type = source.type
else:
element_type = source
if element_type:
input_streams = []
for stream in element_type._selection_streams:
kwargs = dict(source=source)
if isinstance(stream, Selection1D):
kwargs['index'] = None
input_streams.append(stream(**kwargs))
return input_streams
else:
return []
@property
def constants(self):
return {
"source": self.source,
"index_cols": self._index_cols,
"include_region": self.include_region,
}
@property
def source(self):
return Stream.source.fget(self)
@source.setter
def source(self, value):
# Unregister old selection streams
self._unregister_input_streams()
# Set new source
Stream.source.fset(self, value)
# Build selection input streams for new source element
if self.source is not None:
input_streams = self._build_selection_streams(self.source)
else:
input_streams = []
# Clear current selection expression state
self.update(
selection_expr=None,
bbox=None,
region_element=None,
)
# Register callbacks on input streams
self._register_streams(input_streams)
[docs]class SelectionExprSequence(Derived):
selection_expr = param.Parameter(default=None, constant=True)
region_element = param.Parameter(default=None, constant=True)
def __init__(
self, source, mode="overwrite",
include_region=True, **params
):
self.mode = mode
self.include_region = include_region
sel_expr = SelectionExpr(
source, index_cols=params.pop('index_cols'),
**params
)
self.history_stream = History(sel_expr)
input_streams = [self.history_stream]
super(SelectionExprSequence, self).__init__(
source=source, input_streams=input_streams, **params
)
@property
def constants(self):
return {
"source": self.source,
"mode": self.mode,
"include_region": self.include_region,
}
[docs] def reset(self):
self.input_streams[0].clear_history()
super(SelectionExprSequence, self).reset()
[docs]class CrossFilterSet(Derived):
selection_expr = param.Parameter(default=None, constant=True)
def __init__(self, selection_streams=(), mode="intersection", index_cols=None, **params):
self._mode = mode
self._index_cols = index_cols
input_streams = list(selection_streams)
exclusive = mode == "overwrite"
super(CrossFilterSet, self).__init__(
input_streams, exclusive=exclusive, **params
)
@property
def mode(self):
return self._mode
@mode.setter
def mode(self, v):
if v != self._mode:
self._mode = v
self.reset()
self.exclusive = self._mode == "overwrite"
@property
def constants(self):
return {
"mode": self.mode,
"index_cols": self._index_cols
}
[docs] def reset(self):
super(CrossFilterSet, self).reset()
for stream in self.input_streams:
stream.reset()
[docs]class LinkedStream(Stream):
"""
A LinkedStream indicates is automatically linked to plot interactions
on a backend via a Renderer. Not all backends may support dynamically
supplying stream data.
"""
def __init__(self, linked=True, **params):
super(LinkedStream, self).__init__(linked=linked, **params)
[docs]class PointerX(LinkedStream):
"""
A pointer position along the x-axis in data coordinates which may be
a numeric or categorical dimension.
With the appropriate plotting backend, this corresponds to the
position of the mouse/trackpad cursor. If the pointer is outside the
plot bounds, the position is set to None.
"""
x = param.ClassSelector(class_=pointer_types, default=None,
constant=True, doc="""
Pointer position along the x-axis in data coordinates""")
[docs]class PointerY(LinkedStream):
"""
A pointer position along the y-axis in data coordinates which may be
a numeric or categorical dimension.
With the appropriate plotting backend, this corresponds to the
position of the mouse/trackpad pointer. If the pointer is outside
the plot bounds, the position is set to None.
"""
y = param.ClassSelector(class_=pointer_types, default=None,
constant=True, doc="""
Pointer position along the y-axis in data coordinates""")
[docs]class PointerXY(LinkedStream):
"""
A pointer position along the x- and y-axes in data coordinates which
may numeric or categorical dimensions.
With the appropriate plotting backend, this corresponds to the
position of the mouse/trackpad pointer. If the pointer is outside
the plot bounds, the position values are set to None.
"""
x = param.ClassSelector(class_=pointer_types, default=None,
constant=True, doc="""
Pointer position along the x-axis in data coordinates""")
y = param.ClassSelector(class_=pointer_types, default=None,
constant=True, doc="""
Pointer position along the y-axis in data coordinates""")
[docs]class Draw(PointerXY):
"""
A series of updating x/y-positions when drawing, together with the
current stroke count
"""
stroke_count = param.Integer(default=0, constant=True, doc="""
The current drawing stroke count. Increments every time a new
stroke is started.""")
[docs]class SingleTap(PointerXY):
"""
The x/y-position of a single tap or click in data coordinates.
"""
[docs]class Tap(PointerXY):
"""
The x/y-position of a tap or click in data coordinates.
"""
[docs]class DoubleTap(PointerXY):
"""
The x/y-position of a double-tap or -click in data coordinates.
"""
[docs]class PressUp(PointerXY):
"""
The x/y position of a mouse pressup event in data coordinates.
"""
[docs]class PanEnd(PointerXY):
"""The x/y position of a the end of a pan event in data coordinates.
"""
[docs]class MouseEnter(PointerXY):
"""
The x/y-position where the mouse/cursor entered the plot area
in data coordinates.
"""
[docs]class MouseLeave(PointerXY):
"""
The x/y-position where the mouse/cursor entered the plot area
in data coordinates.
"""
[docs]class PlotSize(LinkedStream):
"""
Returns the dimensions of a plot once it has been displayed.
"""
width = param.Integer(None, constant=True, doc="The width of the plot in pixels")
height = param.Integer(None, constant=True, doc="The height of the plot in pixels")
scale = param.Number(default=1.0, constant=True, doc="""
Scale factor to scale width and height values reported by the stream""")
[docs]class SelectMode(LinkedStream):
mode = param.ObjectSelector(default="replace", constant=True, objects=[
"replace", "append", "intersect", "subtract"], doc="""
Defines what should happen when a new selection is made. The
default is to replace the existing selection. Other options
are to append to theselection, intersect with it or subtract
from it.""")
[docs]class RangeXY(LinkedStream):
"""
Axis ranges along x- and y-axis in data coordinates.
"""
x_range = param.Tuple(default=None, length=2, constant=True, doc="""
Range of the x-axis of a plot in data coordinates""")
y_range = param.Tuple(default=None, length=2, constant=True, doc="""
Range of the y-axis of a plot in data coordinates""")
[docs]class RangeX(LinkedStream):
"""
Axis range along x-axis in data coordinates.
"""
x_range = param.Tuple(default=None, length=2, constant=True, doc="""
Range of the x-axis of a plot in data coordinates""")
[docs]class RangeY(LinkedStream):
"""
Axis range along y-axis in data coordinates.
"""
y_range = param.Tuple(default=None, length=2, constant=True, doc="""
Range of the y-axis of a plot in data coordinates""")
[docs]class BoundsXY(LinkedStream):
"""
A stream representing the bounds of a box selection as an
tuple of the left, bottom, right and top coordinates.
"""
bounds = param.Tuple(default=None, constant=True, length=4,
allow_None=True, doc="""
Bounds defined as (left, bottom, right, top) tuple.""")
[docs]class Lasso(LinkedStream):
"""
A stream representing a lasso selection in 2D space as a two-column
array of coordinates.
"""
geometry = param.Array(constant=True, doc="""
The coordinates of the lasso geometry as a two-column array.""")
[docs]class SelectionXY(BoundsXY):
"""
A stream representing the selection along the x-axis and y-axis.
Unlike a BoundsXY stream, this stream returns range or categorical
selections.
"""
x_selection = param.ClassSelector(class_=(tuple, list), allow_None=True,
constant=True, doc="""
The current selection along the x-axis, either a numerical range
defined as a tuple or a list of categories.""")
y_selection = param.ClassSelector(class_=(tuple, list), allow_None=True,
constant=True, doc="""
The current selection along the y-axis, either a numerical range
defined as a tuple or a list of categories.""")
[docs]class BoundsX(LinkedStream):
"""
A stream representing the bounds of a box selection as an
tuple of the left and right coordinates.
"""
boundsx = param.Tuple(default=None, constant=True, length=2,
allow_None=True, doc="""
Bounds defined as (left, right) tuple.""")
[docs]class BoundsY(LinkedStream):
"""
A stream representing the bounds of a box selection as an
tuple of the bottom and top coordinates.
"""
boundsy = param.Tuple(default=None, constant=True, length=2,
allow_None=True, doc="""
Bounds defined as (bottom, top) tuple.""")
[docs]class Selection1D(LinkedStream):
"""
A stream representing a 1D selection of objects by their index.
"""
index = param.List(default=[], allow_None=True, constant=True, doc="""
Indices into a 1D datastructure.""")
[docs]class PlotReset(LinkedStream):
"""
A stream signalling when a plot reset event has been triggered.
"""
resetting = param.Boolean(default=False, constant=True, doc="""
Whether a reset event is being signalled.""")
def __init__(self, *args, **params):
super(PlotReset, self).__init__(self, *args, **dict(params, transient=True))
[docs]class CDSStream(LinkedStream):
"""
A Stream that syncs a bokeh ColumnDataSource with python.
"""
data = param.Dict(constant=True, doc="""
Data synced from Bokeh ColumnDataSource supplied as a
dictionary of columns, where each column is a list of values
(for point-like data) or list of lists of values (for
path-like data).""")
[docs]class PointDraw(CDSStream):
"""
Attaches a PointDrawTool and syncs the datasource.
add: boolean
Whether to allow adding new Points
drag: boolean
Whether to enable dragging of Points
empty_value: int/float/string/None
The value to insert on non-position columns when adding a new polygon
num_objects: int
The number of polygons that can be drawn before overwriting
the oldest polygon.
styles: dict
A dictionary specifying lists of styles to cycle over whenever
a new Point glyph is drawn.
tooltip: str
An optional tooltip to override the default
"""
def __init__(self, empty_value=None, add=True, drag=True, num_objects=0,
styles={}, tooltip=None, **params):
self.add = add
self.drag = drag
self.empty_value = empty_value
self.num_objects = num_objects
self.styles = styles
self.tooltip = tooltip
self.styles = styles
super(PointDraw, self).__init__(**params)
@property
def element(self):
source = self.source
if isinstance(source, UniformNdMapping):
source = source.last
if not self.data:
return source.clone([], id=None)
return source.clone(self.data, id=None)
@property
def dynamic(self):
from .core.spaces import DynamicMap
return DynamicMap(lambda *args, **kwargs: self.element, streams=[self])
[docs]class CurveEdit(PointDraw):
"""
Attaches a PointDraw to the plot which allows editing the Curve when selected.
style: dict
A dictionary specifying the style of the vertices.
tooltip: str
An optional tooltip to override the default
"""
def __init__(self, style={}, tooltip=None, **params):
self.style = style or {'size': 10}
self.tooltip = tooltip
super(PointDraw, self).__init__(**params)
[docs]class PolyDraw(CDSStream):
"""
Attaches a PolyDrawTool and syncs the datasource.
drag: boolean
Whether to enable dragging of polygons and paths
empty_value: int/float/string/None
The value to insert on non-position columns when adding a new polygon
num_objects: int
The number of polygons that can be drawn before overwriting
the oldest polygon.
show_vertices: boolean
Whether to show the vertices when a polygon is selected
styles: dict
A dictionary specifying lists of styles to cycle over whenever
a new Poly glyph is drawn.
tooltip: str
An optional tooltip to override the default
vertex_style: dict
A dictionary specifying the style options for the vertices.
The usual bokeh style options apply, e.g. fill_color,
line_alpha, size, etc.
"""
def __init__(self, empty_value=None, drag=True, num_objects=0,
show_vertices=False, vertex_style={}, styles={},
tooltip=None, **params):
self.drag = drag
self.empty_value = empty_value
self.num_objects = num_objects
self.show_vertices = show_vertices
self.vertex_style = vertex_style
self.styles = styles
self.tooltip = tooltip
super(PolyDraw, self).__init__(**params)
@property
def element(self):
source = self.source
if isinstance(source, UniformNdMapping):
source = source.last
data = self.data
if not data:
return source.clone([], id=None)
cols = list(self.data)
x, y = source.kdims
lookup = {'xs': x.name, 'ys': y.name}
data = [{lookup.get(c, c): data[c][i] for c in self.data}
for i in range(len(data[cols[0]]))]
datatype = source.datatype if source.interface.multi else ['multitabular']
return source.clone(data, datatype=datatype, id=None)
@property
def dynamic(self):
from .core.spaces import DynamicMap
return DynamicMap(lambda *args, **kwargs: self.element, streams=[self])
[docs]class FreehandDraw(CDSStream):
"""
Attaches a FreehandDrawTool and syncs the datasource.
empty_value: int/float/string/None
The value to insert on non-position columns when adding a new polygon
num_objects: int
The number of polygons that can be drawn before overwriting
the oldest polygon.
styles: dict
A dictionary specifying lists of styles to cycle over whenever
a new freehand glyph is drawn.
tooltip: str
An optional tooltip to override the default
"""
def __init__(self, empty_value=None, num_objects=0, styles={}, tooltip=None, **params):
self.empty_value = empty_value
self.num_objects = num_objects
self.styles = styles
self.tooltip = tooltip
super(FreehandDraw, self).__init__(**params)
@property
def element(self):
source = self.source
if isinstance(source, UniformNdMapping):
source = source.last
data = self.data
if not data:
return source.clone([], id=None)
cols = list(self.data)
x, y = source.kdims
lookup = {'xs': x.name, 'ys': y.name}
data = [{lookup.get(c, c): data[c][i] for c in self.data}
for i in range(len(data[cols[0]]))]
return source.clone(data, id=None)
@property
def dynamic(self):
from .core.spaces import DynamicMap
return DynamicMap(lambda *args, **kwargs: self.element, streams=[self])
[docs]class BoxEdit(CDSStream):
"""
Attaches a BoxEditTool and syncs the datasource.
empty_value: int/float/string/None
The value to insert on non-position columns when adding a new box
num_objects: int
The number of boxes that can be drawn before overwriting the
oldest drawn box.
styles: dict
A dictionary specifying lists of styles to cycle over whenever
a new box glyph is drawn.
tooltip: str
An optional tooltip to override the default
"""
def __init__(self, empty_value=None, num_objects=0, styles={}, tooltip=None, **params):
self.empty_value = empty_value
self.num_objects = num_objects
self.styles = styles
self.tooltip = tooltip
super(BoxEdit, self).__init__(**params)
@property
def element(self):
from .element import Rectangles, Polygons
source = self.source
if isinstance(source, UniformNdMapping):
source = source.last
data = self.data
if not data:
return source.clone([])
dims = ['x0', 'y0', 'x1', 'y1']+[vd.name for vd in source.vdims]
if isinstance(source, Rectangles):
data = tuple(data[d] for d in dims)
return source.clone(data, id=None)
paths = []
for i, (x0, x1, y0, y1) in enumerate(zip(data['x0'], data['x1'], data['y0'], data['y1'])):
xs = [x0, x0, x1, x1]
ys = [y0, y1, y1, y0]
if isinstance(source, Polygons):
xs.append(x0)
ys.append(y0)
vals = [data[vd.name][i] for vd in source.vdims]
paths.append((xs, ys)+tuple(vals))
datatype = source.datatype if source.interface.multi else ['multitabular']
return source.clone(paths, datatype=datatype, id=None)
@property
def dynamic(self):
from .core.spaces import DynamicMap
return DynamicMap(lambda *args, **kwargs: self.element, streams=[self])
[docs]class PolyEdit(PolyDraw):
"""
Attaches a PolyEditTool and syncs the datasource.
shared: boolean
Whether PolyEditTools should be shared between multiple elements
tooltip: str
An optional tooltip to override the default
vertex_style: dict
A dictionary specifying the style options for the vertices.
The usual bokeh style options apply, e.g. fill_color,
line_alpha, size, etc.
"""
def __init__(self, vertex_style={}, shared=True, **params):
self.shared = shared
super(PolyEdit, self).__init__(vertex_style=vertex_style, **params)
